Target tracking based on improved square root cubature particle filter via underwater wireless sensor networks

Target tracking in underwater wireless sensor networks (UWSNs) has two fundamental issues that tracking accuracy and energy consumption. Although the application of particle filter in target tracking is considered in recent years, degeneracy phenomenon and particle impoverishment always restrict its capacity and application. This paper proposes an improved square root cubature particle filter (ISRCPF) to improve tracking accuracy. The authors employ self-adaptive artificial fish swarm algorithm (AFSA) to optimise the particles, which makes the particles move towards to high likelihood region and maintains the diversity of the particles. Moreover, a sensor selection scheme is provided, which reduces energy consumption of the network by exactly waking up four sensor nodes at each time, while preserving tracking accuracy. Additionally, the authors propose a novel fusion method called similarity fusion method (SFM) to fuse local estimates together and then obtain a better result for distributed fusion architecture (DFA). The simulation results demonstrate that the proposed methods have superior performance.